High Performance Computing Software Applications Institute

Frederick, MD, United States

High Performance Computing Software Applications Institute

Frederick, MD, United States
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Liu R.,High Performance Computing Software Applications Institute | Tawa G.,High Performance Computing Software Applications Institute | Wallqvist A.,High Performance Computing Software Applications Institute
Chemical Research in Toxicology | Year: 2012

Toxicological experiments in animals are carried out to determine the type and severity of any potential toxic effect associated with a new lead compound. The collected data are then used to extrapolate the effects on humans and determine initial dose regimens for clinical trials. The underlying assumption is that the severity of the toxic effects in animals is correlated with that in humans. However, there is a general lack of toxic correlations across species. Thus, it is more advantageous to predict the toxicological effects of a compound on humans directly from the human toxicological data of related compounds. However, many popular quantitative structure-activity relationship (QSAR) methods that build a single global model by fitting all training data appear inappropriate for predicting toxicological effects of structurally diverse compounds because the observed toxicological effects may originate from very different and mostly unknown molecular mechanisms. In this article, we demonstrate, via application to the human maximum recommended daily dose data that locally weighted learning methods, such as k-nearest neighbors, are well suited for predicting toxicological effects of structurally diverse compounds. We also show that a significant flaw of the k-nearest neighbor method is that it always uses a constant number of nearest neighbors in making prediction for a target compound, irrespective of whether the nearest neighbors are structurally similar enough to the target compound to ensure that they share the same mechanism of action. To remedy this flaw, we proposed and implemented a variable number nearest neighbor method. The advantages of the variable number nearest neighbor method over other QSAR methods include (1) allowing more reliable predictions to be achieved by applying a tighter molecular distance threshold and (2) automatic detection for when a prediction should not be made because the compound is outside the applicable domain. © 2012 American Chemical Society.


Memisevic V.,High Performance Computing Software Applications Institute | Zavaljevski N.,High Performance Computing Software Applications Institute | Pieper R.,J. Craig Venter Institute | Rajagopala S.V.,J. Craig Venter Institute | And 7 more authors.
Molecular and Cellular Proteomics | Year: 2013

Burkholderia mallei is an infectious intracellular pathogen whose virulence and resistance to antibiotics makes it a potential bioterrorism agent. Given its genetic origin as a commensal soil organism, it is equipped with an extensive and varied set of adapted mechanisms to cope with and modulate host-cell environments. One essential virulence mechanism constitutes the specialized secretion systems that are designed to penetrate host-cell membranes and insert pathogen proteins directly into the host cell's cytosol. However, the secretion systems' proteins and, in particular, their host targets are largely uncharacterized. Here, we used a combined in silico, in vitro, and in vivo approach to identify B. mallei proteins required for pathogenicity. We used bioinformatics tools, including orthology detection and ab initio predictions of secretion system proteins, as well as published experimental Burkholderia data to initially select a small number of proteins as putative virulence factors. We then used yeast two-hybrid assays against normalized whole human and whole murine proteome libraries to detect and identify interactions among each of these bacterial proteins and host proteins. Analysis of such interactions provided both verification of known virulence factors and identification of three new putative virulence proteins. We successfully created insertion mutants for each of these three proteins using the virulent B. mallei ATCC 23344 strain. We exposed BALB/c mice to mutant strains and the wild-type strain in an aerosol challenge model using lethal B. mallei doses. In each set of experiments, mice exposed to mutant strains survived for the 21-day duration of the experiment, whereas mice exposed to the wild-type strain rapidly died. Given their in vivo role in pathogenicity, and based on the yeast two-hybrid interaction data, these results point to the importance of these pathogen proteins in modulating host ubiquitination pathways, phagosomal escape, and actin-cytoskeleton rearrangement processes. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc.


Liu R.,High Performance Computing Software Applications Institute | Yu X.,High Performance Computing Software Applications Institute | Wallqvist A.,High Performance Computing Software Applications Institute
Journal of Cheminformatics | Year: 2015

Background: The use of structural alerts to de-prioritize compounds with undesirable features as drug candidates has been gaining in popularity. Hundreds of molecular structural moieties have been proposed as structural alerts. An emerging issue is that strict application of these alerts will result in a significant reduction of the chemistry space for new drug discovery, as more than half of the oral drugs on the market match at least one of the alerts. To mitigate this issue, we propose to apply a rigorous statistical analysis to derive/validate structural alerts before use. Method: To derive human liver toxicity structural alerts, we retrieved all small-molecule entries from LiverTox, a U.S. National Institutes of Health online resource for information on human liver injuries induced by prescription and over-the-counter drugs and dietary supplements. We classified the compounds into hepatotoxic, nonhepatotoxic, and possible hepatotoxic classes, and performed detailed statistical analyses to identify molecular structural fragments highly enriched in the hepatotoxic class beyond random distribution as structural alerts for human liver injuries. Results: We identified 12 molecular fragments present in multiple marketed drugs that one can consider as common "drug-like" fragments, yet they are strongly associated with drug-induced human liver injuries. Thus, these fragments may be considered as robust hepatotoxicity structural alerts suitable for use in drug discovery screening programs. Conclusions: The use of structural alerts has contributed to the identification of many compounds with potential toxicity issues in modern drug discovery. However, with a large number of structural alerts published to date without proper validation, application of these alerts may restrict the chemistry space and prevent discovery of valuable drugs. To mitigate this issue, we showed how to use statistical analyses to develop a small, robust, and broadly applicable set of structural alerts. © 2015 Liu et al.; licensee Springer.

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